Construction tunnel temporary ventilation pipe arrangement optimization method based on genetic algorithm

Abstract

The invention discloses a construction tunnel temporary ventilation pipe arrangement optimization method based on a genetic algorithm. The method comprises the steps of collecting air parameter data at a tunnel face; establishing a temporary ventilation opening position space model according to the ventilation tunnel building construction drawing in the construction period; establishing a temporary ventilation multi-objective optimization model based on tunnel construction related specifications and determining related constraint conditions; solving by adopting an improved genetic algorithm to obtain the optimal arrangement of the temporary ventilation pipes; and performing simulation verification on an optimization result by using numerical simulation software. By constructing the grid model, establishing the multi-objective optimization model and adopting the improved genetic algorithm, arrangement optimization of the temporary ventilation pipe of the construction tunnel is achieved, the ventilation efficiency of a ventilation system is improved, the ventilation cost is reduced, and therefore the overall competitiveness of a construction project is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction ventilation, and particularly relates to a construction tunnel temporary ventilation pipe arrangement optimization method based on a genetic algorithm. BACKGROUND

[0002] In the process of dynamic excavation of the tunnel construction, especially the tunnel face, due to the change of the relative position between the end of the ventilation pipe and the tunnel face, a reasonable ventilation system is crucial for the health and safety of the construction personnel. Especially in the tunnel face area, due to the operation of construction machinery, blasting and manual operation, a large amount of dust, carbon monoxide (CO) and nitrogen dioxide (NO2) and other harmful gases are generated, and the operation of construction equipment and geological conditions also cause heat accumulation, affecting the thermal comfort of workers. After the blasting operation, the dust generated can be controlled by spraying water, but gases are relatively stable, not soluble in water, and have a great impact on human health. In order to dilute harmful gases and improve the thermal environment, a combination of local ventilation and full-face ventilation is usually used to introduce clean air and exhaust polluted air.

[0003] At present, the ventilation of the construction tunnel mainly relies on the fan + air pipe mode, and the common arrangement modes include positive pressure air supply, negative pressure air exhaust or mixed ventilation. In actual application, the arrangement mode of the air pipe has a decisive influence on the ventilation effect.

[0004] The existing ventilation pipe arrangement method mainly has the following problems: 1) the existing method relies on manual judgment of the air pipe position, and the air pipe outlet distance needs to be adjusted regularly, but due to the lack of accurate measurement and calculation, it is difficult to maintain the best ventilation state; manual adjustment is time-consuming and laborious and may reduce the ventilation efficiency, and even aggravate the accumulation of local pollution. 2) The construction tunnel environment is affected by many factors, and the traditional ventilation scheme mainly dilutes CO and NO2 generated by blasting to the safety threshold, and does not fully consider the thermal comfort requirements such as air temperature, humidity and wind speed. Due to the lack of dynamic adaptability in design, it is easy to cause uneven cold and heat, high temperature or excessive cooling, affecting the efficiency and health of workers.

[0005] ​For the regulation of construction tunnel ventilation volume, the invention (CN 117266907 A) proposes a multi-face construction tunnel ventilation system and volume regulation method. The intelligent control of the linkage between the air valves realizes intelligent air distribution for multiple faces, realizes the mutual use of adjacent tunnel ventilation devices, and can change the air distribution volume in real time according to the working conditions of each face. The invention (CN 118445892 B) proposes a design method and system for dispersion oxygen supply terminals in construction tunnels. In the actual construction process, the different concentrated positions of workers are established. The correlation formula of oxygen concentration and the dimensionless design parameters of the dispersion oxygen supply terminal is established. Through derivation, the design parameter value of the dispersion oxygen supply terminal when the oxygen concentration reaches the maximum is obtained, and targeted oxygen supply is effectively carried out. It can be seen that the existing scheme lacks aerodynamic calculation and numerical simulation optimization based on actual working conditions. This leads to uneven air volume distribution, unreasonable air flow at the face, and the generation of ventilation dead angles, which affects dust control and discharge.

[0006] Genetic Algorithm (GA) is an optimization algorithm that simulates natural selection and genetic principles, belonging to evolutionary computation methods. It is used to solve complex optimization problems, especially suitable for solving large spaces, complex problems and without explicit analytical solutions. In order to solve the problems of existing tunnel construction ventilation methods in ventilation effect, control and construction personnel thermal comfort protection. The invention introduces a genetic algorithm-based construction tunnel temporary ventilation pipe layout optimization method.

[0007] The principle of genetic algorithm is to constantly evolve and adapt through initialization of population, fitness evaluation, selection, crossover, mutation and other steps, and finally find the approximate optimal solution of the optimization problem. The advantage of genetic algorithm is that it can perform global search in a large range of solution space, adapt to complex multi-peak functions and high-dimensional problems. The invention applies genetic algorithm to construction tunnel temporary ventilation pipe layout optimization, which helps to quickly and dynamically adjust the ventilation system, improve the working environment quality of construction personnel, and improve the overall energy efficiency of the ventilation system. SUMMARY

[0008] The main purpose of the present invention is to provide a genetic algorithm-based construction tunnel temporary ventilation pipe layout optimization method to solve the problems of existing tunnel construction ventilation methods in ventilation effect, control and construction personnel thermal comfort protection.

[0009] To achieve the above purpose, the present invention provides the following technical solutions:

[0010] An example of the present invention provides a genetic algorithm-based construction tunnel temporary ventilation pipe layout optimization method, which includes the following steps:

[0011] Through the installation of temperature and humidity sensors and gas detectors at key positions in the tunnel, real-time monitoring of environmental data such as the tunnel face, entrances and exits, and ventilation ducts is carried out, and these data are used to verify the optimization effect of the temporary ventilation duct arrangement.

[0012] Further, the establishment of the temporary ventilation port position space model comprises the following specific steps:

[0013] A space coordinate system with a corner of the construction tunnel tunnel face as the origin is adopted, point mass assumption is used, the air port is regarded as a model with the mass center concentrated on a point, and the actual size and shape of the air port are ignored to establish the temporary ventilation port position model:

[0014] (1);

[0015] wherein, is the horizontal position of the ventilation port on the tunnel section, is all position points where the air port can appear; is the position point of the air port in the construction tunnel space coordinate system axis direction; is the position point of the air port in the construction tunnel space coordinate system axis direction; is the longitudinal distance of the ventilation port from the tunnel face, is the position point of the air port in the construction tunnel space coordinate system axis direction.

[0016] Further, to achieve the three goals of improving the thermal comfort of workers, reducing the concentration of the work area and saving ventilation costs, a temporary ventilation multi-objective optimization model is established and relevant constraint conditions are determined, comprising the following specific steps:

[0017] A tunnel face work area prediction index model is established:

[0018] (2);

[0019] wherein, is the air temperature near the tunnel face; is the radiant temperature; is the worker construction metabolic rate; is the worker clothing thermal resistance; is the clothing thermal resistance correction coefficient; is the wind speed correction coefficient, is the work area plane average wind speed; is the clothing thermal resistance correction coefficient;

[0020] Further, the is introduced, and the objective function is established:

[0021] (3);

[0022] wherein, is another index related to , representing the proportion of people who feel uncomfortable under given environmental conditions, the smaller the value, the higher the comfort of the environment;

[0023] Further, the objective function is established:

[0024] (4);

[0025] wherein, is the ratio of the volume fraction measurement value to the allowable value, the smaller the value, the lower the concentration of the working area; is the ratio of the volume fraction measurement value to the allowable value, the smaller the value, the lower the concentration of the working area; is the average volume fraction of the tunnel cross section when the ventilation time is , and the distance from the tunnel face is ; is the average volume fraction of the tunnel cross section when the ventilation time is , and the distance from the tunnel face is ; is the required volume fraction, the contact time is less than ; is the required volume fraction, the contact time is less than ;

[0026] Further, the objective function is established:

[0027] (5);

[0028] wherein, is the material cost of the air duct, is the cost of the unit diameter air duct; is the diameter of the air duct; is the length of the air duct; is the purchase and operation cost of the fan, is the cost of the unit air volume of the fan, is the required air volume; is the power and operation cost, is the unit power cost; is the fan power; is the operation time; , is the auxiliary equipment cost per unit of air duct diameter and length;

[0029] Further, the following multiple constraint conditions are considered in the optimization process:

[0030] Ventilation equipment selection, temporary ventilation volume, air duct diameter, air duct suspension position, and distance from the tunnel face of the air outlet.

[0031] Further, the improved genetic algorithm for solving multi-objective optimization function includes the following specific steps:

[0032] The dynamic game theory is applied to deal with the multi-objective problem of air pipe arrangement, and a dynamic game model for air pipe arrangement optimization is established.

[0033] (6) ;

[0034] Wherein, is the air pipe arrangement optimization objective function; is the sub-game perfect Nash equilibrium; is the interest corresponding to the interest function in the dynamic game theory;

[0035] Further, the sub-game perfect Nash equilibrium solution of the model is the optimization result, and the detailed algorithm process is shown in the specific implementation method.

[0036] Further, the temporary air pipe arrangement optimization method combined with the actual case includes the following specific steps:

[0037] A certain construction tunnel is selected as the basis of the temporary air pipe arrangement optimization space model. The temporary air pipe is connected from the tunnel entrance to the tunnel terminal face, and the tunnel cross section and longitudinal length are represented by 、 The equation is:

[0038] (7) ;

[0039] Wherein, is the maximum width of the tunnel; is the maximum height of the tunnel; is the maximum length of the tunnel;

[0040] Further, the point sequence of the air outlet position is taken as the coding of the chromosome individual, and the initial point coordinates and the terminal point coordinates are added to form a complete point sequence, which is represented by a coordinate matrix:

[0041] (8) ;

[0042] Wherein, is the chromosome of the air outlet position; is the th point of the air outlet position; is the temporary air volume change value of the fan; is the initial point when the temporary air volume is ; is the last group of points when the temporary air volume is ;

[0043] Further, the greedy initialization method is adopted to generate an initial population, and the layout optimization of the tuyere point needs to meet a proximity, that is, the current tuyere point selects the point closest to it, and an optimal solution can be obtained by rearranging the order;

[0044] Further, the advantages and disadvantages of the individuals in the initial population are sorted and layered, the ranking and the crowding distance are combined for sorting, and the objective function value is combined for evaluating each chromosome, the dominance relationship between the chromosomes is considered, and the chromosomes are assigned a rank;

[0045] Further, in order to obtain a superior population, an elite strategy is adopted, the parent and child populations are combined, the non-dominated sorting and the calculation of the crowding distance are performed, the individuals with low rank and large crowding distance are preferentially selected, when the upper limit is exceeded, the individuals are screened according to the crowding distance, the selection operation adopts a binary tournament strategy, two individuals are randomly selected, and the individual with low RANK or large CROWD is preferentially selected;

[0046] Further, the crossover point crossover and the random point crossover based on the asymmetric single-point crossover strategy are adopted, wherein the asymmetric refers to different lengths of the chromosomes, and the crossover position is not fixed;

[0047] Further, for the point layout coding, a mutation mode of "randomly deleting a section and adding a section" is adopted, two mutation positions (not including the starting and target points) are randomly selected on the crossover chromosome, a sub-point is generated, and the original point is replaced;

[0048] Further, in order to improve the quality of the solution and avoid falling into a local optimum, a local search mechanism is introduced into the algorithm, the taboo search is adopted to avoid returning to a local optimum point, and the search efficiency is improved;

[0049] Further, the new individuals are composed of the next generation through selection, crossover and mutation, the old population is replaced, the operation is repeated, the Nash equilibrium solution of each generation is solved, and the optimal solution is selected, and finally the optimal Nash equilibrium solution set is output as the optimization result of the construction tunnel temporary ventilation pipe layout.

[0050] Further, the effects of the pressurized air supply before and after the optimization of the temporary ventilation pipe of the construction period tunnel are respectively simulated by a simulation software, and the optimization effect is verified.

[0051] Compared with the prior art, the present application has the following remarkable effects:

[0052] (1) In the present application, a temporary ventilation port position space model is established;

[0053] (2) A multi-objective optimization model of the temporary ventilation pipe layout optimization is constructed, and the related constraint conditions are determined. The improved genetic algorithm is used for multiple operations, and the optimal solution of the temporary ventilation pipe layout of the construction tunnel is obtained.

[0054] (3) Using simulation software to simulate before and after optimization, verifying the feasibility and effectiveness of optimization.

[0055] (4) By constructing a grid model, establishing a multi-objective optimization model, and using an improved genetic algorithm, the layout optimization of the temporary ventilation pipe of the construction tunnel is realized, the ventilation efficiency of the ventilation system is improved, the ventilation cost is reduced, and the overall competitiveness of the construction project is improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0057] Figure 1 The step flow chart for the method for optimizing the layout of the temporary ventilation pipe of the construction tunnel of the present application is shown in the figure.

[0058] Figure 2 The schematic diagram of the spatial model of the temporary ventilation port is shown in the figure.

[0059] Figure 3 The schematic diagram of the improved genetic algorithm flow is shown in the figure.

[0060] Figure 4 The schematic diagram of the simulation results of the simulation software before and after optimization is shown in the figure. DETAILED DESCRIPTION

[0061] In order to make the features and advantages of the present application more obvious and easy to understand, the following embodiments are described in detail as follows:

[0062] The technical solutions of the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. The components described and indicated in the drawings herein can be designed in different configurations. Therefore, the following detailed description of the selected embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only to represent selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0064] As Figure 1As shown, this example proposes a multi-objective optimization method for the layout of temporary ventilation pipes in construction tunnels based on genetic algorithms. The layout method includes:

[0065] Step S1: collecting air parameter data at the tunnel face.

[0066] By placing temperature, humidity and carbon monoxide concentration detection sensors at different locations in the tunnel, the environmental data in the tunnel can be obtained in real time. , digital humidity sensor uses , the gas detector uses a high-precision carbon monoxide detection sensor Temperature and humidity sensors and gas detectors were installed at key locations within the tunnel, such as the tunnel face, entrances and exits, and ventilation ducts. Real-time data monitoring was used to verify the optimization effect of the temporary ventilation duct layout in the construction tunnel.

[0067] Step S2: establishing a temporary ventilation opening location spatial model according to the ventilation tunnel construction drawing during the construction period.

[0068] like Figure 2 As shown in the figure, a spatial coordinate system with a corner of the tunnel face as the origin is established. Using the point mass assumption, the vent is regarded as a model with the center of mass concentrated at one point, ignoring the actual size and shape of the vent, and a temporary vent position model is established. :

[0069] (1).

[0070] in, is the horizontal position of the ventilation opening on the tunnel section, All possible locations where tuyere may appear; The spatial coordinate system of the vent in the construction tunnel Point position in the axis direction; The spatial coordinate system of the vent in the construction tunnel Point position in the axis direction; is the longitudinal distance between the ventilation opening and the tunnel face, The spatial coordinate system of the vent in the construction tunnel Points in the axis direction.

[0071] The establishment of the temporary ventilation position spatial model is thus completed. The initial temporary ventilation position is not optimal to meet the workers' construction thermal comfort index and construction location. To address issues such as high concentration and high cost of forced ventilation, it is necessary to use a genetic algorithm model combined with three-party game theory to set the corresponding objective function and constraints as the basic model for subsequent optimization.

[0072] Step S3, establish a temporary ventilation multi-objective optimization model and determine the related constraints based on the tunnel construction related specifications.

[0073] Ideally, the tunnel temperature suitable for workers should be maintained at 15-20℃, and the temperature of the working face should not exceed 26℃; too low air humidity (less than 30%) will cause dry skin, and too high humidity (greater than 80%) will make people feel uncomfortable, and the appropriate range is 50-60%; wind speed affects human heat dissipation, too high makes people feel cold or uncomfortable, and too low makes it difficult to promote heat exchange, which may cause personnel overheating, and the wind speed range is controlled at 0.1-1.5 m / s, and the best is 0.3 m / s.

[0074] For the quantitative evaluation of thermal comfort, the working area of the working face is established based on the relevant specifications of the building engineering environment (such as "Building Construction Environment Control Standard" GB 5083-2010, "Human Thermal Comfort Standard" ISO 7730, etc.), and the working area of the working face is divided into three zones: the first zone is the working area of the working face, the second zone is the area between the working face and the ventilation pipe, and the third zone is the area between the working face and the ventilation pipe. Prediction index model:

[0075] (2).

[0076] wherein, is the air temperature near the working face; is the radiation temperature; is the worker's construction metabolic rate; is the worker's clothing thermal resistance; is the clothing thermal resistance correction coefficient; is the wind speed correction coefficient, is the average wind speed of the working area plane; is the clothing thermal resistance correction coefficient.

[0077] Since the value is usually between -3 and +3, it cannot accurately reflect the effect of each optimization of the ventilation pipe arrangement, therefore is introduced, and the objective function is established:

[0078] (3).

[0079] wherein, is another index related to , indicating the proportion of people who feel uncomfortable under given environmental conditions, and the smaller the value, the higher the comfort of the environment.

[0080] After the blasting operation, the dust generated can be controlled by spraying water, while the state of the gas is relatively stable and cannot be dissolved in water, and has a great impact on the human body. In order to reduce the average concentration at the working face, the objective function is established:

[0081] (4).

[0082] in, for The ratio of the volume fraction measurement value to the allowed value. The smaller the value, the greater the working area. The lower the concentration; The ventilation time is , the distance from the tunnel face is The average tunnel cross section Volume fraction; To meet the requirements Volume fraction: 0.008% for contact time less than 30 min, and 0.0024% for long-term contact.

[0083] The selection of appropriate ventilation equipment can effectively improve ventilation efficiency and save ventilation energy. Theoretically, the larger the ventilation volume, the higher the ventilation efficiency. However, the actual project should consider the cost of ventilation investment comprehensively. 、 On this basis, we further reduce the temporary ventilation cost and establish the objective function :

[0084] (5).

[0085] in, is the material cost of the air duct, is the cost of the air duct per unit diameter; is the diameter of the wind tube; is the length of the hair dryer; The purchase and operation costs of the fan, is the fan cost per unit air volume, is the required air volume; For electricity and running costs, is the unit electricity cost; is the fan power; is the running time; , The cost of auxiliary equipment per unit diameter and length of the wind tube.

[0086] To meet the temporary ventilation requirements of construction tunnels, improve workers' thermal comfort and reduce working area In order to achieve the three goals of concentration and saving ventilation costs, the following constraints must be fully considered during the optimization process:

[0087] a, Ventilation equipment selection: Ventilation duct and fan belong to ventilation equipment, select duct first and then fan, appropriate selection of ventilation equipment can effectively improve the ventilation efficiency and save the ventilation energy consumption. In this example, DSD-2 type soft duct and DFZ-9 type centrifugal fan are used.

[0088] b, Temporary ventilation volume: The larger the ventilation volume, the higher the ventilation efficiency in theory, but the actual project should consider the cost of ventilation investment. The temporary ventilation volume of this example is . , which needs to meet the minimum ventilation volume required by the specification; and the maximum air supply volume of the fan.

[0089] c, Duct diameter: Larger duct diameter can reduce ventilation resistance loss and improve ventilation efficiency, but it will also increase ventilation cost. In this example, the DSD-2 type soft duct is used, and the duct diameter D is 500mm-2000mm.

[0090] d, Duct suspension position: Different duct suspension positions will affect the distribution of jet flow and vortex flow in the tunnel, and then affect the diffusion and emission of harmful gases. In this example, the duct suspension position is the point set formed by the air outlet on the tunnel cross section.

[0091] e, Distance between air outlet and tunnel face: The distance between the air outlet and the tunnel face directly affects the ventilation effect. If the air outlet is too close to the tunnel face, the jet flow cannot develop fully, and the ventilation effect is poor; if it is too far away from the tunnel face, the jet flow may not reach the tunnel face, resulting in reduced dilution and emission efficiency of harmful gases. In this example, the distance between the air outlet and the tunnel face is , where is the maximum length of the tunnel.

[0092] Step S4, solve the multi-objective optimization model using improved genetic algorithm.

[0093] The temporary ventilation pipe layout of the construction tunnel needs to optimize multiple objectives, such as improving thermal comfort, reducing concentration, reducing ventilation cost, etc. These objectives may conflict and need to be balanced. Each objective can be regarded as a "participant" to achieve its own objective by affecting the design variables, and they affect each other, similar to the interaction in game theory.

[0094] To apply dynamic game theory to solve the multi-objective problem of ventilation pipe layout, a dynamic game model for tunnel temporary ventilation pipe layout optimization needs to be established. In this example, three indicators are selected, representing the three participants in the dynamic game: the first game player's benefit function is the optimal thermal comfort index , the second is the minimum ventilation cost, and the third is the minimum concentration of harmful gases.​ concentration (C) , the third is the minimum ventilation cost (C ). The dynamic game model of ventilation duct layout optimization is:

[0095] (6).

[0096] wherein, is the ventilation duct layout optimization objective function; is the sub-game perfect Nash equilibrium; is the benefit corresponding to the benefit function in dynamic game theory.

[0097] The sub-game perfect Nash equilibrium solution of the model is the optimization result. The specific algorithm flow is shown in Figure 3 , and the basic steps are described as follows:

[0098] S41, use software to construct the spatial modeling of the construction tunnel and the temporary ventilation duct layout, and extract the spatial geometric information.

[0099] S42, algorithm parameter setting, input the starting point coordinate information of the ventilation port and include the improved genetic algorithm and Nash equilibrium solving method.

[0100] S43, population initialization.

[0101] S44, population evaluation, calculate the value of each individual corresponding to the three objective functions.

[0102] S45, use the Nash equilibrium solving method to find the Nash equilibrium solution in the current generation, and record it.

[0103] S46, judge whether the stopping criterion is met, if yes, go to S408, otherwise go to the next step.

[0104] S47, form a new population according to the improved hybrid genetic algorithm.

[0105] S48, genetic operation to form a new population, including recombination, crossover and mutation.

[0106] S49, local search for each individual.

[0107] S410, return to step S403.

[0108] S411, compare all the Nash equilibrium solutions recorded in each generation, and select the optimal solution.

[0109] S412, output the optimal Nash equilibrium solution set.

[0110] The standard formula for judging Nash equilibrium is as follows:

[0111] (7).

[0112] wherein, is the th individual Nash equilibrium evaluation criterion; is the th individual's performance on the th objective relative to the optimal value, i.e. its relative error ratio, such as a negative value indicates that the individual performs worse than the optimal value on the objective; a positive value indicates that the individual performs better than the optimal value on the objective; is the th objective value of the th individual; is the optimal value of the th objective.

[0113] The basic flow of the Nash equilibrium solving method in S45 is as follows:

[0114] S451, use the improved genetic algorithm to optimize the ventilation pipe arrangement to obtain the optimal value of each objective function .

[0115] S452, calculate the Nash equilibrium solution of each individual in the current population.

[0116] S453, find the optimal Nash equilibrium and assign it to .

[0117] S454, compare the of each individual with .

[0118] S455, judge , if not, go to S456, otherwise go to S459.

[0119] S456, calculate and judge , if yes, go to the next step, otherwise go to S458.

[0120] S457, record , and let , return to S454.

[0121] S458, , return to S454.

[0122] S459, output the Nash equilibrium solution of the population.

[0123] The Nash equilibrium solution of each generation recorded by the algorithm can be used for final result selection. In the optimization of ventilation pipe arrangement, each objective is constantly approaching its optimal solution, but no single objective can dominate the global decision.

[0124] Step S5: Apply the temporary ventilation pipe layout optimization method in combination with actual cases.

[0125] S51, ventilation duct layout space pretreatment

[0126] In this example, a tunnel in Hebei during construction period is selected as the basis for the optimization space model of temporary ventilation pipe layout. The temporary ventilation pipe runs from the tunnel entrance to the tunnel end face. 、 The cross-section and longitudinal length of the tunnel are represented by the equation:

[0127] (8).

[0128] in, is the maximum width of the tunnel; is the maximum height of the tunnel; The maximum length of the tunnel.

[0129] S52, chromosomally encoded

[0130] The point sequence of the air outlet position is used as the encoding of the chromosome individual, and the initial point coordinates and the end point coordinates are added to form a complete point sequence. Formula (9) represents the encoding of multiple temporary ventilation volumes, which is represented by a coordinate matrix:

[0131] (9).

[0132] in, Chromosomes for vent locations; The first position of the tuyere Group point location; is the temporary ventilation volume change value of the fan; Temporary ventilation volume is The initial point at time; Temporary ventilation volume is The last set of points at .

[0133] S53, population initialization

[0134] The greedy initialization method is used to generate the initial population. The layout optimization of the outlet points needs to meet a proximity, that is, the current outlet point selects the next point closest to it, and a solution can be optimized by rearranging the order. The specific steps are: let the group size be , first generate Individuals at the vent points, The starting points of each individual are Then use the greedy method to generate the remaining individuals.

[0135] S54, calculate the target value of the individual population

[0136] The individuals in the initial population are ranked and stratified based on their quality. The ranking can be performed by combining the rank and crowding distance. At the same time, each chromosome is evaluated based on the objective function value, and the dominance relationship between chromosomes is considered to assign a rank to the chromosomes. The specific steps are as follows:

[0137] S541, let the chromosomes in the population that are not dominated by any individual be the first level and record them in the set , the remaining chromosomes are recorded in the set .

[0138] S542, Evaluation Set The chromosomes that are not controlled by individuals are recorded as the second level.

[0139] S543, repeat the above steps until the last individual.

[0140] After sorting all chromosomes in ascending order, in order to maintain population diversity, the crowding distance is added. If the levels are the same, the crowding distance is compared. The larger the crowding distance, the better the solution. For individuals The crowding distance of an individual is calculated as follows:

[0141] (10).

[0142] in, and Individual and In the The value of the objective function; and 1 for all individuals in the The maximum and minimum values ​​on the objective function, the crowding distance of the individual on the edge is .

[0143] S55, Selection and Elite Strategy

[0144] In order to obtain the dominant population, the elite strategy is adopted. First, the parent population and offspring population Merge into a new population And sort by non-dominated, then calculate the crowding distance and sort. Prioritize individuals with lower ranks and larger crowding distances to enter the new population If individuals of the same non-dominant level exceed the upper limit , then filter by crowding distance until the number is The selection operation adopts a binary tournament strategy, randomly selecting two individuals and giving priority to Low value or Individuals with large values ​​( Indicates the sorting level, Reflects the degree of crowding around an individual (calculated as shown in formulas 11-13). This method ensures population diversity while maintaining individuals with high fitness.

[0145] (11).

[0146] in, is the position of the individual in the sorted sequence (from arrive , is the population size); For individuals value.

[0147] (12).

[0148] (13).

[0149] in, For individuals The crowding distance value, and Individual The fitness values ​​of the next neighbor and the previous neighbor, and are the maximum and minimum fitness values ​​respectively.

[0150] S56, hybridization operation

[0151] Crossover and random dot hybridizations based on an asymmetric single-dot hybridization strategy were used. Asymmetry refers to the fact that chromosomes can have different lengths and the hybridization position is not fixed.

[0152] Crossover hybridization: first list the parents and A common set of points (excluding the starting and target sites) as potential hybridization sites. Randomly select a point As the hybridization point, all the points after this point are exchanged to generate a new chromosome.

[0153] Random dot hybridization: in the parent generation and Randomly select two hybridization positions (excluding the starting and target sites) on the and As the starting point and end point, the sub-points are constructed using the initial point generation method. Then, the daughter sites are connected at the hybridization point, and all sites after the hybridization point are exchanged to generate daughter chromosomes.

[0154] S57, mutation operation

[0155] For the site layout coding, a "random deletion and addition" mutation method is used. After hybridization, two mutation positions (excluding the start and target sites) are randomly selected on the chromosome to generate sub-sites and replace the original site.

[0156] The acceptance operator uses the principle of dominant individual acceptance. For offspring individuals, if their fitness is higher than that of their parents, they advance to the next generation; otherwise, the parents remain. Furthermore, fitness evaluation is embedded in the key engineering constraints for ventilation duct layout optimization. Individuals that violate these constraints are assigned low fitness values, guiding the population toward feasible solutions while gradually eliminating infeasible solutions.

[0157] S58, local search

[0158] To improve solution quality and avoid falling into local optima, the algorithm incorporates a local search mechanism. Taboo search avoids local optima by using a tabu table and an expectation criterion. Optimizing vent locations is a discrete combinatorial optimization problem, and using tabu search can avoid returning to local optima and improve search efficiency.

[0159] S59, iterative solution

[0160] The new individuals generated by selection, crossover and mutation form the next generation and replace the old population. The above operations S53 to S58 are repeated to solve the Nash equilibrium solution of each generation and select the optimal solution. The optimal Nash equilibrium solution set is output as the final result of the optimization of the layout of temporary ventilation pipes in the construction tunnel.

[0161] Step S6: Use simulation software to simulate and verify the optimization results.

[0162] After inputting the tunnel model parameters, the initial air parameters of the tunnel face, and the initial parameters of the temporary ventilation ducts, the maximum number of iterations was set to 100, the population size to 50, the crossover probability to 0.8, and the mutation probability to 0.3. The optimized layout of the temporary ventilation ducts in the construction tunnel was calculated using an improved genetic algorithm.

[0163] Fluent software was used to simulate the effects of forced-in ventilation before and after optimization of temporary ventilation pipes in the tunnel during construction. The air parameters and The concentration results are as follows Figure 4 As shown in the figure, it can be seen that before optimization, the temperature of the working area at the tunnel face was about 23.5℃, and the relative humidity was about 72.0%. The concentration is about 0.00256%; the temperature at the tunnel face after optimization is about 20.3%, and the relative humidity is about 65%. The concentration is about 0.00202%. According to the simulation results, the improved genetic algorithm has certain effect on the temporary ventilation pipe arrangement optimization, can effectively improve the thermal comfort index of construction workers, reduce the working area plane Concentration and save certain ventilation cost.

[0164] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or can be implemented by means of software and necessary general hardware platforms. Based on such understanding, the technical solutions of the above embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.), and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application.

[0165] The above is only a preferred embodiment of the present application, and is not intended to limit the other forms of the present application. Any person skilled in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments. However, any simple modification, equivalent change and modification made without departing from the technical solution of the present application, according to the technical essence of the present application, still belongs to the protection scope of the present application.

[0166] The present application is not limited to the above optimal implementation, and anyone can derive other various forms of a layout optimization method of an underground space water spray system under the inspiration of the present application. Any equivalent change and modification made according to the scope of the present application should be within the scope of the present application.

Claims

1. A method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithms, characterized in that: The method comprises the following steps: Collect air parameter data at the tunnel face and use the data to verify the optimization effect of temporary ventilation pipe layout; According to the construction drawings of the ventilation tunnel during the construction period, a temporary ventilation opening location spatial model is established as the basic model for subsequent optimization; Establish a temporary ventilation multi-objective optimization model based on relevant tunnel construction specifications, establish the optimization objective function and determine the relevant constraints; An improved genetic algorithm is used to solve the multi-objective optimization model. The Nash equilibrium solution of each generation of the population recorded by the algorithm can be used to select the final result. In the ventilation duct layout optimization, each objective continuously approaches its own optimal solution, but no single objective can dominate the overall decision. Combined with actual cases, the temporary ventilation pipe layout optimization method is applied to solve the Nash equilibrium solution of each generation and select the optimal solution. The optimal Nash equilibrium solution set is output as the final result of the temporary ventilation pipe layout optimization in the construction tunnel. Use simulation software to simulate the air supply effect before and after ventilation duct optimization to verify the optimization results.

2. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithm according to claim 1, characterized in that: The spatial coordinate system with a corner of the construction tunnel face as the origin adopts the point mass assumption and regards the vent as a model with the center of mass concentrated at one point. The actual size and shape of the vent are ignored to establish the temporary vent location model: （1）； in, is the horizontal position of the ventilation opening on the tunnel section, All possible locations where tuyere may appear; The spatial coordinate system of the vent in the construction tunnel Point position in the axis direction; The spatial coordinate system of the vent in the construction tunnel Point position in the axis direction; is the longitudinal distance between the ventilation opening and the tunnel face, The spatial coordinate system of the vent in the construction tunnel Points in the axis direction.

3. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithm according to claim 1, characterized in that: To improve workers' thermal comfort and reduce working area A temporary ventilation multi-objective optimization model is established based on the three objectives of concentration and ventilation cost saving, and the relevant constraints are determined. The specific steps are as follows: Establish the first objective function: （3）； in, For Another related indicator represents the proportion of people who feel uncomfortable under given environmental conditions. The smaller the value, the higher the comfort level of the environment. Establish the second objective function (4); in, for The ratio of the volume fraction measurement value to the allowable value. The smaller the value, the greater the working area. The lower the concentration; The ventilation time is , the distance from the tunnel face is The average tunnel cross section Volume fraction; To meet the requirements Volume fraction, contact time less than Pick Long-term contact ; Establish the third objective function (5); in, is the material cost of the air duct, is the cost of the air duct per unit diameter; is the diameter of the wind tube; is the length of the hair dryer; The purchase and operation costs of the fan, is the fan cost per unit air volume, is the required air volume; For electricity and running costs, is the unit electricity cost; is the fan power; is the running time; , is the cost of auxiliary equipment per unit diameter and length of the wind tube; The following constraints are considered during the optimization process: They are the selection of ventilation equipment, temporary ventilation volume, duct diameter, duct hanging position, and the distance between the air outlet and the tunnel face.

4. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithm according to claim 1, characterized in that: The improved genetic algorithm is used to solve the multi-objective optimization function, and the specific steps include the following: Dynamic game theory is applied to deal with the multi-objective problem of duct layout, and a dynamic game model for ventilation duct layout optimization is established: （6）； in, Optimize the objective function for ventilation duct layout; It is a subgame perfect Nash equilibrium; is the interest corresponding to the interest function in dynamic game theory.

5. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithm according to claim 1, characterized in that: The above-mentioned temporary ventilation pipe layout optimization method is applied in combination with actual cases, and the specific steps include the following: A construction tunnel was selected as the basis for the optimization spatial model of temporary ventilation pipe layout. The temporary ventilation pipe runs from the tunnel entrance to the tunnel end face. 、 The cross-section and longitudinal length of the tunnel are represented by the equation: （7）； in, is the maximum width of the tunnel; is the maximum height of the tunnel; The maximum length of the tunnel.

6. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithm according to claim 5, characterized in that: The point sequence of the vent position is used as the encoding of the chromosome individual, and the initial point coordinates and the end point coordinates are added to form a complete point sequence, which is represented by a coordinate matrix: （8）； in, Chromosomes for vent locations; The first position of the tuyere Group point location; is the temporary ventilation volume change value of the fan; Temporary ventilation volume is The initial point at time; Temporary ventilation volume is The last set of points at .

7. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithm according to claim 6, characterized in that: The greedy initialization method is used to generate the initial population. The layout optimization of the outlet points needs to meet a proximity, that is, the current outlet point selects the next point closest to it, and a solution can be optimized by rearranging the order.

8. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithms according to claim 7, characterized in that: The individuals in the initialized population are ranked and stratified according to their quality, and the ranking is performed based on the rank and crowding distance. At the same time, each chromosome is evaluated based on the objective function value, and the dominance relationship between chromosomes is considered to assign a rank to the chromosomes.

9. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithms according to claim 1, characterized in that: In order to obtain the dominant population, an elite strategy is adopted to merge the parent and offspring populations, sort them by non-dominated order and calculate the crowding distance. Individuals with low rank and large crowding distance are given priority. When the upper limit is exceeded, they are screened by crowding distance. The selection operation adopts a binary tournament strategy to randomly select two individuals and select the individual with low RANK or large CROWD.

10. The method for optimizing the layout of temporary ventilation pipes in construction tunnels based on genetic algorithms according to claim 9, characterized in that: Crossover hybridization and random dot hybridization based on asymmetric single-point hybridization strategy were used, where asymmetry refers to the different lengths of chromosomes and non-fixed hybridization positions; For the site layout coding, a "random deletion and addition" mutation method was used. Two mutation positions that did not contain the start and target sites were randomly selected on the chromosome after hybridization to generate sub-sites and replace the original site. In order to improve the quality of the solution and avoid falling into the local optimum, the algorithm introduces a local search mechanism and adopts taboo search to avoid returning to the local optimum, thereby improving the search efficiency. New individuals form the next generation through selection, crossover, and mutation, replacing the old population. The process is repeated to solve the Nash equilibrium solution of each generation and select the optimal solution. Finally, the optimal Nash equilibrium solution set is output as the optimization result of the temporary ventilation pipe layout in the construction tunnel. The simulation software was used to numerically simulate the effects of forced-in air supply before and after optimization of the temporary ventilation duct in the tunnel during the construction period to verify the optimization effect.

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